scholarly journals TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

2021 ◽  
Author(s):  
Shreshth Tuli ◽  
Rajas Bansal ◽  
Rohan Paul ◽  
Mausam .
Keyword(s):  
Neural Network ◽  
Neural Model ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Commonsense Knowledge ◽  
Symbolic Action ◽  
World State ◽  
The World ◽  
Human Teachers ◽  
High Level

Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce TANGO, a novel neural model for predicting task-specific tool interactions. TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator. TANGO encodes the world state consisting of objects and symbolic relationships between them using a graph neural network. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show a 60.5-78.9% improvement over the baseline in predicting successful symbolic plans in unseen settings for a simulated mobile manipulator.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2011 ◽  
pp. 310-325
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2009 ◽  
Vol 3 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this article, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Mobile Operations Performed by Mobile Manipulators on Irregular Terrain - Torque Compensation Using Neural Networks for Disturbance Torques Produced by Irregular Terrain -

1998 ◽  
Vol 10 (5) ◽  
pp. 377-386 ◽  
Author(s):  
Mamoru Minami ◽  
◽  
Masatoshi Hatano ◽  
Toshiyuki Asakura ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Horizontal Plane ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Adaptive Control System ◽  
Control Performance ◽  
Irregular Terrain

In the present study, we propose a control system for mobile operations of mobile manipulators traveling on irregular terrain. Irregularities exist even in structures such as man-made floors of factories and buildings. Since the hand of a mobile manipulator is often required to operate precisely while traveling on irregular terrain and it is subject to disturbance torques caused by traveling on terrain, a method for decreasing control errors caused by disturbances due to terrain must be considered. In the present paper, an adaptive control system including a compensator that uses a neural network, i.e., a neuro adaptive control system, is proposed. In addition, we discuss the control performance of the proposed control system, and show that the control system can decrease control errors occurring on irregular terrain to the levels of errors that occur while traveling on a horizontal plane.

Decentralized Dynamic Control of a Nonholonomic Mobile Manipulator Collective: A Simulation Study

2008 ◽  
Author(s):  
Hao Su ◽  
Venkat Krovi
Keyword(s):  
Control Algorithm ◽  
Null Space ◽  
Dynamic Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Level Control ◽  
Individual Agent ◽  
End Effector ◽  
High Level ◽  
Task Accomplishment

In this paper, we present a decentralized dynamic control algorithm for a robot collective consisting of multiple nonholonomic wheeled mobile manipulators (NH-WMMs) capable of cooperatively transporting a common payload. In this algorithm, the high level controller deals with motion/force control of the payload, at the same time distributes the motion/force task into individual agents by grasp description matrix. In each individual agent, the low level controller decomposes the system dynamics into decoupled task space (end-effector motions/forces) and a dynamically-consistent null-space (internal motions/forces) component. The agent level control algorithm facilitates the prioritized operational task accomplishment with the end-effector impedance-mode controller and secondary null-space control. The scalability and modularity is guaranteed upon the decentralized control architecture. Numerical simulations are performed for a 2-NH-WMM system carrying a payload (with/without uncertainty) to validate this approach.

scholarly journals An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Kong ◽  
Qingqing Tang ◽  
Jingsheng Lei ◽  
Ruiyang Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Matrix Equation ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Time Varying ◽  
Repetitive Motion ◽  
End Effector ◽  
The Matrix ◽  
Simulation Results

A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.

Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

2018 ◽  
Vol 8 (5) ◽  
pp. 10
Author(s):  
Revati Kadu ◽  
U. A. Belorkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Disease ◽  
Skin Diseases ◽  
Screening Method ◽  
Phase Detection ◽  
Statistical Measures ◽  
The World ◽  
Artificial Neural ◽  
Wavelet Techniques

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Her Majesty Queen Rania Al Abdullah

2019 ◽  
Author(s):  
Adib Rifqi Setiawan
Keyword(s):  
Education System ◽  
Ministry Of Education ◽  
Childhood Experiences ◽  
The Sustainable Development ◽  
American University ◽  
The North ◽  
The World ◽  
Development Goals ◽  
High Level ◽  
Education And Development

“The real treasure is in the minds of our children, and all we have to do is extract it.” Her Majesty Queen Rania Al Abdullah writes in website Queen Rania Foundation For Education And Development www.qrf.org/en. Rania Al Yassin was born on August 31, 1970. She obtained her Bachelor’s degree in Business Administration from the American University of Cairo in 1991. She applied this, first, to a banking career in Jordan and, later, to the information technology sector. After marrying Prince Abdullah bin Al Hussein on June 10, 1993, they went on to have four children: Prince Hussein, Princess Iman, Princess Salma, and Prince Hashem. In addition to being a wife and mother, Queen Rania works hard to lift the lives of Jordanians by supporting their endeavours and helping to create new opportunities for them. Locally, she is committed to breathe new life into the public education system; empower communities and women especially through microfinance initiatives; protect children and families; and drive innovation, technology and entrepreneurship, especially amongst young people. Internationally, Queen Rania is an advocate for tolerance, compassion and bridge building between people of all cultures and backgrounds. Her efforts to simultaneously challenge stereotypes of Arabs and Muslims, and promote greater understanding and acceptance between people of all faiths and cultures, have won her global recognition. Her Majesty’s passion is education. She believes that every Jordanian girl and boy, and all children, should have access not only to stimulating classrooms and modern curricula, but inspiring teachers and technology that can connect Jordan’s children to the world and the world to Jordan’s children. Her efforts in the education sector complement the work of the Ministry of Education through initiatives such as the Jordan Education Initiative, the Queen Rania Teachers Academy, Madrasati, Edraak and others. To realize these and so much more, Queen Rania has encouraged private sector partners to drive improvements and strengthen the foundations of Jordan’s education system. Queen Rania is also a global voice for access to quality education for children around the world. In 2009, Her Majesty championed the 1 Goal campaign for education; she is Honorary Chair of the UN Girl’s Education Initiatives and has advocated access to education in forums and gatherings around the world. Her work and her efforts to improve the learning opportunities for children have been recognized at the highest levels, nationally, regionally and internationally. Additionally, through her position on their boards, Her Majesty contributes to the work of the United Nations Fund and the World Economic Forum. She is the Eminent Advocate for UNICEF; and she was part of the UN appointed High Level Panel who advised on the shape and content of the Sustainable Development Goals which aim to improve the lives of millions of people before 2030. In recognition of her work, Her Majesty has humbly accepted many awards, locally, regionally and globally. These include the Walther Rathenau Award from the Walther RathenauInstitut in Germany for her efforts to greater peace and understanding; the James C. Morgan Global Humanitarian Award from Tech Awards, USA; the Arab Knight of Giving Award from Arab Giving Forum, UAE; the North South Prize by the North South Prize, Portugal; as well as the YouTube Visionary Award. Her Majesty authored several books primarily for children including the Sandwich Swap, which was inspired by her own childhood experiences.

Global warming influences dynamics of plague’s epidemic manifestations in XX-XXI centuries

2017 ◽  
pp. 38-40
Author(s):  
E.S. Zenkevich ◽  
N.V. Popov
Keyword(s):  
Global Warming ◽  
20Th Century ◽  
Modern Trend ◽  
Climate Fluctuations ◽  
The World ◽  
Low Incidence ◽  
High Level

During the second half of 20th century, a high level of plague incidence in the world was in 1960–1979 and 1990–2009. The significant decrease of infection cases was in 1950–1959, 1980–1989, 2010–2015. It is noticed, that the observed cyclical nature of the alternation of high and low incidence plague’s periods, in many respects related to modern trend of climate fluctuations.

SYNONYMY OF STABLE PHRASES IN THE DICTIONARY OF SMOLENSK DIALECTS: THE ANALYSIS EXPERIENCE

2019 ◽  
pp. 122-132
Author(s):  
Nina Maksimchuk
Keyword(s):  
Native Speakers ◽  
National Language ◽  
Cultural Value ◽  
Semantic Structure ◽  
Spiritual World ◽  
Key Word ◽  
The World ◽  
Volumetric Representation ◽  
High Level ◽  
Linguistic Units

The attention of modern linguistics to the study of verbal representatives of the mental essence (both individual and collective one) of the native speakers involves an appeal to all subsystems of the national language where territorial dialects take a significant part. The analysis of dialect linguistic units possessing linguistic and cultural value is considered as a necessary way for the study of people’s worldview and perception of the world, national mentality as a whole. The ability of stable phrases (phraseological units) to preserve and express a native speaker’s attitude to the world around them is the basis for the use of the analysis of folk phraseology as a way of penetration into a speaker’s spiritual world. Volumetric representation of the external and internal peculiarities of stable phrases allows the author to get their systematization in the form of phraseosemantic field consisting of different kinds singled out in phraseosemantic groups. The article deals with stable phrases of synonymic value recorded in the Dictionary of Smolensk dialects and stable phrases forming a phraseosemantic group. These phrases are analyzed taking into account the semantic structure of the key word, the characteristics of the dependent word, and the method of forming phraseological semantics. On the example of the analysis of phrases with the key word «bit’» and a synonymic series with the semantic dominant «bezdel’nichat’», the article discusses the peculiarities of phraseological nomination in Smolensk dialects and confirms a high level of connotativity and evaluation in the folk phraseology.

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